98%
921
2 minutes
20
Background: Pediatric convulsive status epilepticus (CSE) is a neurological emergency utilizing electroencephalography (EEG) to guide therapeutic interventions. Guidelines recommend EEG initiation within one hour of seizure onset, but logistic and structural barriers often lead to significant delays. We aimed to reduce the time to EEG in pediatric CSE.
Methods: From 2017 to 2022, we implemented process improvements, including EEG order sets with priority-based timing guidance, technologist workflow changes, a satisfaction survey, and feedback from key stakeholder groups, over five plan-do-study-act (PDSA) cycles. Seizure start time, time of EEG order, and time to EEG initiation were extracted. Time to interpretable EEG was determined from manual review of the EEG tracing.
Results: Time from EEG order to interpretable EEG decreased by nearly 50%, from a median of 90 minutes to 48 minutes. There were clinically and statistically significant improvements in time from EEG order to EEG initiation, time from EEG order to interpretable EEG, and EEG start to interpretable EEG. Ongoing provider education and guidance enabled improvements, whereas a new electronic health care record negatively impacted electronic ordering. EEG technologists reported that they understood the importance of emergent EEG for clinical care and did not find that the new workflow caused excessive disruption.
Conclusions: Timely access to EEG for pediatric patients with CSE can be improved through clinical processes that use existing devices and that maintain the benefits of full-montage EEG recordings. Similar process improvement efforts may be generalizable to other institutions to increase adherence to guidelines and provide improved care.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.pediatrneurol.2024.01.006 | DOI Listing |
Exp Brain Res
September 2025
Siena Brain Investigation and Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy.
Postdiction is a perceptual phenomenon where the perception of an earlier stimulus is influenced by a later one. This effect is commonly studied using the 'rabbit illusion', in which temporally regular, but spatially irregular, stimuli are perceived as equidistant. While previous research has focused on short inter-stimulus intervals (100-200 ms), the role of longer intervals, which may engage late attentional processes, remains unexplored.
View Article and Find Full Text PDFEpileptic Disord
September 2025
Department of Neurology, Neurocritical Care and Neurorehabilitation, Christian Doppler University Hospital, Centre for Cognitive Neuroscience, Member of the European Reference Network EpiCARE, Paracelsus Medical University of Salzburg, Salzburg, Austria.
J Integr Neurosci
August 2025
School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, Guangdong, China.
Background: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition.
View Article and Find Full Text PDFFront Neurosci
August 2025
Department of Neurology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China.
Objective: Construct a predictive model for rehabilitation outcomes in ischemic stroke patients 3 months post-stroke using resting state functional magnetic resonance imaging (fMRI) images, as well as synchronized electroencephalography (EEG) and electromyography (EMG) time series data.
Methods: A total of 102 hemiplegic patients with ischemic stroke were recruited. Resting - state functional magnetic resonance imaging (fMRI) scans were carried out on all patients and 86 of them underwent simultaneous electroencephalogram (EEG) and electromyogram (EMG) examinations.
Data Brief
October 2025
Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar.
PhysioPain dataset comprises several physiological data of different kinds of pain: no pain, headache, menstrual cycle pain and back/neck/waist pain in search of a sophisticated and complete approach to pain representation. The study comprised 99 individuals, of whom 93 participants contributed real-time physiological data. These participants underwent experiment process to gather real-time physiological data including electroencephalogram (EEG), skin temperature, electrodermal activity (EDA), blood volume pulse (BVP), and accelerometer data.
View Article and Find Full Text PDF